Communication signal decoding with iterative cooperation between turbo and reed-solomon decoding

ABSTRACT

Received communication signals may be decoded according to a combined turbo-RS (Reed-Solomon) decoding technique. The turbo decoding is based on information produced by the RS decoding.

RELATED APPLICATIONS

This application claims the benefit of priority from U.S. Provisional Patent Application Ser. No. 61/048,483, filed Apr. 28, 2008 and entitled “Interactive Soft Input Decoding of Outer Block Codes and Inner Codes In Concatenated Systems,” which is fully incorporated herein by reference for all purposes.

BACKGROUND

1. Field

The present disclosure relates generally to communications and, more particularly, to coding/decoding schemes for use in communications.

2. Background

The documents listed below are incorporated herein by reference:

-   -   [1] S. Lin and D. J. Costello, “Error Control Coding:         Fundamentals and Applications”, 1st ed. Prentice Hall, 1983.     -   [2] G. D. Forney, “Generalized minimum distance decoding,” IEEE         Trans. Information Theory, vol. 12, pp. 125-131, April 1996.     -   [3] D. Chase, “Class of algorithms for decoding block codes with         channel measurement information,” IEEE Trans. Information         Theory, vol. 18, pp. 170-182, January 1972.     -   [4] M. P. C. Fossorier and S. Lin, “Soft-decision decoding of         linear block codes based on ordered statistics,” IEEE Trans.         Information Theory, vol. 41, pp. 1379-1396, September 1995.     -   [5] R. Koetter and A. Vardy, “Algebraic soft-decision decoding         of Reed-Solomon codes,” IEEE Transactions on Information Theory,         vol. 49, pp. 2809-2825, November 2003.     -   [6] A. Vardy and Y. Be'ery, “Bit-level soft-decision decoding of         Reed-Solomon codes,” IEEE Trans. Communications, vol. 39, pp.         440-444, March 1991.     -   [7] J. Jiang and K. R. Narayanan, “Iterative         soft-input-soft-output decoding of Reed-Solomon codes by         adapting the parity check matrix,” IEEE Trans. Information         Theory, vol. 52, no. 8, pp. 3746-3756, August 2006.     -   [8] J. Jiang, “Advanced Channel Coding Techniques Using         Bit-level Soft Information,” Ph.D dissertation, Dept. of ECE,         Texas A&M University.     -   [9] Jason Bellorado, Aleksandar Kavcic, Li Ping, “Soft-Input,         Iterative, Reed-Solomon Decoding using Redundant Parity-Check         Equations”, Invited paper, IEEE Inform. Theory Workshop (ITW),         Lake Tahoe, Calif., USA, Sep. 2-6, 2007     -   [10] T. J. Richardson, A. Shokrollahi, and R. Urbanke, “Design         of capacity-approaching low-density parity-check codes,” IEEE         Trans. Inform. Theory, vol. 47, pp. 619-637, February 2001.     -   [11] D. J. C. MacKay, “Good error-correcting codes based on very         sparse matrices,” IEEE Trans. Inform. Theory, vol. 45, pp.         399-431, March 1999.     -   [12] R. G. Gallager, Low-Density Parity-Check Codes. Cambridge,         MA: MIT Press, 1963.     -   [13] M. R. Chari, F. Ling, A. Mantravadi, R. Krishnamoorthi, R.         Vijayan, G. K. Walker, and R. Chandhok, “FLO physical layer: An         Overview,” IEEE Trans. Broadcast., vol. 53, no. 1, pt. 2, pp.         145-160, March 2007.

FIG. 1 diagrammatically illustrates a prior art coding/decoding scheme for use in a communication system. The arrangement of FIG. 1 uses a concatenated coding structure with turbo coding for an inner code and Reed-Solomon (RS) coding for an outer code. At the transmitter, designated generally at 11, K data source packets are input to an outer RS encoder block 12. The RS encoder 12 takes the block of K input packets and encodes parities to create additional (N−K) parity packets. All the packets output by the RS encoder 12 are byte-level interleaved at 13, and then encoded through an inner turbo encoder 14. All the turbo encoded packets produced by the turbo encoder 14 are bit-level interleaved and modulated (not explicitly shown), and then transmitted through a noisy communication channel shown diagrammatically at 15. The receiver, designated generally at 16, implements the appropriate demodulation and bit-level de-interleaving (not explicitly shown), and includes a turbo decoder 17 that generates log likelihood ratios (LLRs) that respectively correspond to the turbo coded bits that arrive at the turbo decoder 17. The turbo decoder 17 updates the LLR values iteratively until all the cyclic redundancy checks (CRC's) are satisfied or the maximum number of iterations is reached. Hard decisions regarding the bits of successfully decoded packets are de-interleaved at 18. An RS erasure decoder 19 performs erasure decoding to recover the erased packets if possible. All decoded packets are then passed from the RS decoder 19 to an upper layer at 10. The aforementioned documents designated as [1], [13] (and references therein) provide descriptions of the type of coding/decoding scheme shown in FIG. 1.

If (N, K) is the dimension of the RS code being used at the symbol-level (in bytes), then the RS code rate is R_(RS)=K/N. Some prior art systems support multiple code rates so, for example, K=8, 12, 14, or 16 can be used.

The encoding operation of an (N, K) RS code in the aforementioned concatenated coding system (12 in FIG. 1) is illustrated in FIG. 2. Each row in the data block 21 of FIG. 2 represents is a physical layer packet, and each column contains one byte from each of the packets. The first K packets from the top are the systematic packets from the source (see also FIG. 1). The RS encoder acts along each column of data, i.e. it looks at the K systematic bytes in a column and adds (N−K) parity bytes per column. Thus, for an (N, K) code, there would be N physical layer packets at the output of the RS encoder 12 of FIG. 1. The column-wise operation of the RS encoder 12 constitutes an implicit byte interleaving.

Referring again to FIG. 1, at the RS decoder 19, the turbo-decoded physical layer packets belonging to one interleaver block (e.g., the block 21 of FIG. 2) are first stored in a buffer. The CRC of each of the physical layer packets in the buffer is computed to determine whether the packet has been received correctly or not. If the CRC indicates an error, the entire packet is treated as an erasure. Each column of the block is an RS codeword. On the other hand, each row is a single physical layer packet, which is either received correctly or is declared to be an erasure. Thus, each RS codeword in the same RS block contains the same number of erasures in exactly the same positions. This structure can be used to further simplify the erasure decoding algorithm by using a single “generator matrix” to compute the erased locations from K non-erased ones for all the RS codewords. An (N, K) RS code has a redundancy of (N−K) bytes, and is therefore able to correct any combination of (N−K) or fewer erasures within a codeword. However, if more than (N−K) packets in the block are erased, there is no attempt to recover the erased packets in erasure decoding.

It is desirable in view of the foregoing to provide for decoding that is capable of recovering erasures that are lost by the prior art approach.

SUMMARY

Received communication signals may be decoded according to a combined turbo-RS (Reed-Solomon) decoding technique. The turbo decoding is based on information produced by the RS decoding.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of a wireless communications system are illustrated by way of example, and not by way of limitation, in the accompanying drawings, wherein:

FIG. 1 diagrammatically illustrates a prior art coding/decoding scheme for use in a communication system.

FIG. 2 illustrates the operation of an (N, K) Reed-Solomon code.

FIG. 3 diagrammatically illustrates exemplary embodiments of coding/decoding schemes for use in a communication system according to the present work.

FIG. 4 diagrammatically illustrates a portion of prior art FIG. 1.

FIG. 5 diagrammatically illustrates a portion of FIG. 3 according to exemplary embodiments of the present work.

FIGS. 6-10 illustrate various prior art decoding techniques applicable to the bipartite graph structure of a linear block code.

FIG. 11, when considered together with FIG. 5, diagrammatically illustrates a portion of FIG. 3 according to exemplary embodiments of the present work.

FIG. 12, when considered together with FIG. 5, diagrammatically illustrates a portion of FIG. 3 according to exemplary embodiments of the present work.

FIG. 13 diagrammatically illustrates exemplary embodiments of a communication system according to the present work.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appended drawings is intended as a description of various embodiments of the invention and is not intended to represent the only embodiments in which the invention may be practiced. For example, even though the preferred embodiment is described in the context of using a Turbo code as the inner code and Reed-Solomon code as the outer code, it should be apparent to those skilled in the part that Thr inner code could be a convolutional code or a block code and the outer code can also be a general block code (such as RS, BCH, Hamming code and etc.)

The detailed description includes specific details for the purpose of providing a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring the concepts of the invention.

The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

The present work recognizes that the prior art concatenated turbo-RS coding structure (such as shown in FIG. 1) does not fully exploit the potential error correction capability of the overall turbo-RS code. In this prior art approach, whenever the turbo decoder fails to converge to satisfy all the CRC's, the relevant soft information available to the RS decoder is discarded. Exemplary embodiments of the present work provide improved decoding performance by implementing iterative decoding between a turbo decoder and an RS decoder. Soft decision decoding of the RS code is used to support the iterative decoding between the turbo and RS decoders. This soft decision decoding utilizes the soft information available to the RS decoder.

RS codes can be viewed as non-binary Bose-Chaudhuri and Hocquenghem (BCH) codes. Consider an RS (N, K) code over GF(q) with a minimum distance d_(min)=N−K+1, its parity check matrix can be represented as:

$H_{S} = \begin{pmatrix} 1 & \alpha^{b} & \ldots & \alpha^{{({N - 1})}b} \\ 1 & \alpha^{b + 1} & \ldots & \alpha^{{({N - 1})}{({b + 1})}} \\ 1 & \; & ⋰ & \; \\ 1 & \alpha^{b + N - K} & \ldots & \alpha^{{({N - 1})}{({b + N - K})}} \end{pmatrix}$

where α is a primitive element in GF(q) and b is an integer number.

It is known that all the 2 ^(m) elements in GF(2 ^(m)), namely, 0, 1, α, α², . . . , α² ² ⁻², can be represented by an m-dimensional binary vector expansion in GF(2) using a basis which spans GF(2 ^(m)). Addition operation in GF(2 ^(m)) is simply the component wise addition of the vectors over GF(2). Similarly, multiplication can be achieved by matrix multiplication. Each entry in the parity check matrix H_(s) can be represented in the binary form using an m×m matrix over GF(2). For instance, for RS codes over GF(4), where α is a root of the primitive polynomial p(x)=x²+x+1, α has the binary vector representation [0,1]. The multiplication operation ×α corresponds the binary multiplication of the vector expansion with a multiplication matrix

$\times {\begin{pmatrix} 0 & 1 \\ 1 & 1 \end{pmatrix}.}$

In other words, an RS code can also be viewed as a binary linear block code. Therefore, RS codes over GF(2 ^(m)), which are used in many communication systems, such as the current FLO system, can be represented using equivalent binary image expansions. Let n=N×m and k=K×m be the length of the codeword and the message at the bit-level, respectively. H_(s) has an equivalent binary image expansion H_(b), where H_(b) is an (n−k)×n binary parity check matrix. For example, a shortened (16, 12) RS code has an equivalent binary image form, which is a binary (128, 96) code.

Due to the binary image form of RS codes over GF(2 ^(m)), and further because the soft information available to the RS decoder is in the form of an LLR of each bit, many decoding algorithms proposed for binary codes become directly applicable to RS codes over GF(2 ^(m)). Bit-level soft decision decoding algorithms have been found to be efficient in decoding RS codes over GF(2 ^(m)).

FIG. 3 diagrammatically illustrates a coding/decoding scheme for use in a communication system (e.g., a wireless communication system) according to exemplary embodiments of the present work. Some embodiments use the prior art transmitter shown generally at 11 in FIG. 1. The receiver, designated generally at 36, includes an iterative turbo-RS decoder 31 that receives turbo coded bits in the same manner as received by the turbo decoder 17 of FIG. 1. The iterative turbo-RS decoder applies iterative turbo-RS decoding to the received turbo coded bits in order to produce hard decisions that are passed to the upper layer 10 (see also FIG. 1). The structure and operation of the iterative turbo-RS decoder 31 can be understood in the context of the known structure and operation of the prior art decoding arrangement of FIG. 1.

FIG. 4 diagrammatically illustrates the components 17-19 of FIG. 1 in more detail. The turbo decoder 17 includes an LLR generator 41, a de-puncturing unit 42, maximum a posteriori (MAP) probability decoders 43 and 44, interleaver 45, de-interleaver 46, and multiplexer 47, arranged and interconnected in conventional fashion as shown. The de-interleaver 18 (see also FIG. 1) de-interleaves the decoded bits from MAP decoder 44, and feeds them to one input of a multiplexer 48 (not explicitly shown in FIG. 1) whose other input receives the decoded bits from the MAP decoder 43. The turbo decoder 17 receives data samples and channel estimate information, and employs conventional turbo decoding (utilizing the signals labeled as systematic, parity 0, parity 1, Input_APP0, Input_App1, Output_EXT0, and Output_EXT1) to produce the conventional turbo output at 49. This turbo output 49 is provided to the RS erasure decoder 19.

FIG. 5 diagrammatically illustrates the iterative turbo-RS decoder 31 (see also FIG. 3) according to exemplary embodiments of the present work. The iterative turbo-RS decoder 31 differs from the conventional decoding arrangement of FIG. 4 in that a RS SISO (soft input-soft output) decoder 51 and a modified turbo decoder 55 are combined for iterative decoding operations via a linking structure 56. RS SISO decoders such as shown at 51 are known in the art. In various embodiments, the RS SISO decoder 51 of FIG. 5 is implemented according to prior art techniques described in various ones of the documents incorporated by reference hereinabove (such as [2]-[9]).

The linking structure 56 of FIG. 5 includes a summing element 57 that receives extrinsic LLRs (designated as EXT-RS) produced by the RS SISO decoder 51. Instead of applying the systematic LLRs to the systematic input 49 of the MAP decoder 43 as in FIG. 4, the summing element 57 adds the systematic LLRs to the extrinsic LLRs EXT_RS, and applies the addition results to the systematic input 49 of the MAP decoder 43. The MAP decoders 43 and 44 perform the same iterative turbo decoding as in FIG. 4. As in FIG. 4, the extrinsic LLRs, Output_EXT0 and Output_EXT1, produced respectively by the MAP decoders 43 and 44, are used for iterative turbo decoding. However, these LLRs Output_EXT0 and Output_EXT1 are also applied to a summing element 58 of the linking structure 56. A further summing element 59 of the linking structure 56 subtracts the LLRs EXT_RS (generated from the previous RS SISO iteration) from the output results of the summing element 58. The subtraction results are provided to the RS SISO decoder 51 as a priori LLR inputs APP_RS. Every time the LLR's corresponding to a packet of Turbo coded bits are processed by MAP decoder 0 or MAP decoder 1, either the decoded bits 0 or the decoded bits 1 are feed to the multiplexor 48. Hard decisions of the output of 48 are checked by the CRC block XX. The hard decisions are provided to the upper layer 10 (see also FIG. 4) if the CRC is passed or the maximum iterations are reached. Otherwise, the Turbo-RS decoder continues to the next processing step.

Various embodiments employ various measures to improve performance. For example, when the CRC of a turbo packet are satisfied, all the bits in that packet may be considered to be correctly received and this information can be used to improve the decoding performance or simplify decoding (for example, the corresponding LLR's may be fixed to be a large value). Moreover, further turbo decoding on this packet may be skipped. The number of turbo iterations in turbo decoder 55, the number of RS iterations (in embodiments that use iterative RS decoding) in RS decoder 51, and the number of turbo-RS iterations between the turbo and RS decoders 55 and 51 may be adjusted to achieve desired performance and complexity tradeoffs.

Some embodiments exploit the bipartite graph structure of a linear block code. Recall that an RS code over GF(2 ^(m)) can be expanded using its binary image expansion and hence can be viewed as a binary linear block code. A binary parity check matrix is associated with any given RS code over GF(2 ^(m)). Therefore, iterative decoding techniques can be applied to the bipartite graph of a binary linear block code, e.g., an RS code.

The bipartite graph structure of a binary linear block code is known in the art as shown in FIG. 6. The circles on the left, variable nodes, are coded bits of the codeword, and the squares on the right, check nodes, are the single parity check constraints coded bits have to satisfy. Variable nodes and check nodes are connected via edges in the middle at 61. Iterative decoding is performed on the bipartite graph using the LLRs of coded bits as inputs 62. The bipartite graph structure of FIG. 6 supports conventional belief propagation (BP) based decoding, as described below.

Conventional BP based decoding, as supported by the bipartite graph structure of FIG. 6, is composed of two steps, variable node update and check node update. As shown in FIG. 7, in the variable node update step, the updated variable-to-check node LLR ν_(j) associated with the jth edge is calculated as the summation of the channel LLR ν_(ch) and all check-to variable node LLRs u_(i) connected to that variable node except for the jth check-to-variable LLR u_(j). Equation (1) below defines this calculation:

$\begin{matrix} {v_{j} = {u_{ch} + {\sum\limits_{i \neq j}u_{i}}}} & (1) \end{matrix}$

FIG. 8 graphically illustrates an example of the check node update step. The updated check-to-variable node LLR (e.g., u₂ in FIG. 8) is calculated according to equation (2) below:

$\begin{matrix} {u_{j} = {2{\tanh^{- 1}\left( {\prod\limits_{i \neq j}{\tanh \left( \frac{v_{i}}{2} \right)}} \right)}}} & (2) \end{matrix}$

For a low complexity implementation, some embodiments modify the variable node update and the check node update as shown in the examples of FIGS. 9 and 10, respectively. The modified variable node update, referred to as a belief accumulation update, calculates the updated channel LLR at the Ith RS SISO iteration, u_(ch) ^((l+1)), according to equation (3) below:

$\begin{matrix} {u_{j}^{({l + 1})} = {u_{ch}^{({l + 1})} = {u_{ch}^{(l)} + {\alpha {\sum\limits_{i}u_{i}^{(l)}}}}}} & (3) \end{matrix}$

where α is a scaling factor. The updated variable-to-check node LLR at the (l+1)th RS SISO iteration, u_(j) ^((l+1)), is simply the updated channel LLR u_(ch) ^((l−1)). FIG. 9 shows the use of equation (3) to compute the updated variable-to-check node LLR u₂ ^((l+1)). The modified check node update computes the updated check-to-variable node LLR according to the so-called min-sum algorithm, shown in equation (4) below:

$\begin{matrix} {u_{j} = {\prod\limits_{i \neq j}{{{sgn}\left( v_{i} \right)}{\min\limits_{i \neq j}\left( {v_{i}} \right)}}}} & (4) \end{matrix}$

FIG. 10 shows the use of equation (4) to compute the updated check-to-variable node LLR u₂. These reduced complexity approximations of the variable node and check node updates require less memory and facilitate hardware implementations.

In various embodiments, the RS SISO decoder 51 implements iterative decoding using various combinations of the decoding algorithms shown in equations (1)-(4) and FIGS. 7-10 to compute the variable and check node updates.

Referring again to FIG. 5, it will be noted that the linking structure 56 is configured to apply BP based decoding to the RS-to-turbo update, because the summing element 57 functions according to BP based updating rule, and is configured to apply BP to the turbo-to-RS update because the summing element 59 functions as BP.

FIG. 11 diagrammatically illustrates embodiments that apply BA based updating rule to the RS-to-turbo update. In FIG. 11, instead of receiving EXT_RS directly from the RS SISO decoder 51 as in FIG. 5, the input APP_Turbo of summing element 57 receives the output of a summing element 110. The summing element 110 adds the current LLR EXT_RS to the sum of all the previous LLR EXT_RSes, which is stored in a register 111.

FIG. 12 diagrammatically illustrates embodiments that apply BA based updating rule to the turbo-to-RS update. In FIG. 12, instead of providing the subtraction result from summing element 59 to the APP_RS input of RS SISO decoder 51 as in FIG. 5, the output result of summing element 58 is applied directly to the APP_RS input.

Some embodiments use the BP based updating rule of FIG. 5 for the turbo-to-RS update, and use the BA based updating rule of FIG. 11 for the RS-to-turbo update. Some embodiments use the BP based updating rule of FIG. 5 for the RS-to-turbo update, and use the BA based updating rule of FIG. 12 for the turbo-to-RS update. Some embodiments use the BA based updating rule of FIG. 11 for the RS-to-turbo update, and use the BA based updating rule of FIG. 12 for the turbo-to-RS update.

FIG. 13 diagrammatically illustrates an example of a communications system 100 that may utilize the decoding techniques described above. The communications system 100 creates and broadcasts multimedia content across various networks to a large number of mobile subscribers. The communications system 100 includes any number of content providers 102, a content provider network 104, a broadcast network 106, and a wireless access network 108. The communications system 100 is also shown with a number of devices 110 used by mobile subscribers to receive multimedia content. According to various embodiments of the present work, these devices 110 may utilize therein the various ones of the decoding techniques described above. The devices 110 include a mobile telephone 112, a personal digital assistant (PDA) 114, and a laptop computer 116. The devices 110 illustrate just some of the devices that are suitable for use in the communications systems 100. It should be noted that although three devices are shown in FIG. 13, virtually any number of analogous devices or types of devices are suitable for use in the communications system 100, as would be apparent to those skilled in the art.

The content providers 102 provide content for distribution to mobile subscribers in the communications system 100. The content may include video, audio, multimedia content, clips, real-time and non real-time content, scripts, programs, data or any other type of suitable content. The content providers 102 provide content to the content provider network for wide-area or local-area distribution.

The content provider network 104 comprises any combination of wired and wireless networks that operate to distribute content for delivery to mobile subscribers. In the example illustrated in FIG. 13, the content provider network 104 distributes content through a broadcast network 106. The broadcast network 106 comprises any combination of wired and wireless proprietary networks that are designed to broadcast high quality content. These proprietary networks may be distributed throughout a large geographic region to provide seamless coverage to mobile devices. Typically, the geographic region will be divided into sectors with each sector providing access to wide-area and local-area content.

The content provider network 104 may also include a content server (not shown) for distribution of content through a wireless access network 108. The content server communicates with a base station controller (BSC) (not shown) in the wireless access network 108. The BSC may be used to manage and control any number of base transceiver station (BTS)s (not shown) depending on the geographic reach of the wireless access network 108. The BTSs provide access to wide-area and local-area for the various devices 110.

The multimedia content broadcast by the content providers 102 include one or more services. A service is an aggregation of one or more independent data components. Each independent data component of a service is called a flow. By way of example, a cable news service may include three flows: a video flow, an audio flow, and a control flow.

Services are carried over one or more logical channels. A logical channel may be divided into multiple logical sub-channels. These logical sub-channels are called streams. Each flow is carried in a single stream. The content for a logical channel is transmitted through the various networks in a physical frame, sometimes referred to as a superframe.

The air interface used to transmit the physical frames to the various devices 110 shown in FIG. 13 may vary depending on the specific application and the overall design constraints. Some embodiments utilize Orthogonal Frequency Division Multiplexing (OFDM), which is also utilized by Digital Audio Broadcasting (DAB), Terrestrial Digital Video Broadcasting (DVB-T), and Terrestrial Integrated Services Digital Broadcasting (ISDB-T). OFDM is a multi-carrier modulation technique that effectively partitions the overall system bandwidth into multiple (N) sub-carriers. These sub-carriers, which are also referred to as tones, bins, frequency channels, etc., are spaced apart at precise frequencies to provide orthogonality. Content may be modulated onto the sub-carriers by adjusting each sub-carrier's phase, amplitude or both. Typically, quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM) is used, but other modulation schemes may also be used.

Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. 

1. An apparatus for processing data signals transmitted on a communication link, comprising: an input for receiving the data signals; a turbo decoder coupled to said input and configured to perform turbo decoding; and a Reed-Solomon (RS) decoder coupled to said turbo decoder and configured to perform RS decoding; wherein said turbo decoding is based on RS decoding information produced by said RS decoding.
 2. The apparatus of claim 1, wherein said RS decoding information includes extrinsic information produced by said RS decoding.
 3. The apparatus of claim 2, wherein said RS decoding is based on extrinsic information produced by said turbo decoding.
 4. The apparatus of claim 1, wherein said RS decoding is based on extrinsic information produced by said turbo decoding.
 5. The apparatus of claim 1, wherein said RS decoder is a soft input-soft output (SISO) decoder.
 6. The apparatus of claim 1, including a belief propagation updating rule coupled between said turbo decoder and said RS decoder, and configured to apply belief propagation to one of extrinsic information produced by said RS decoding and extrinsic information produced by said turbo decoding.
 7. The apparatus of claim 6, including a further belief propagation updating rule coupled between said turbo decoder and said RS decoder, and configured to apply belief propagation to the other of said extrinsic information produced by said RS decoding and said extrinsic information produced by said turbo decoding.
 8. The apparatus of claim 1, including a belief accumulation updating rule coupled between said turbo decoder and said RS decoder, and configured to apply belief accumulation to one of extrinsic information produced by said RS decoding and extrinsic information produced by said turbo decoding.
 9. The apparatus of claim 8, including a belief propagation updating rule coupled between said turbo decoder and said RS decoder, and configured to apply belief propagation to the other of said extrinsic information produced by said RS decoding and said extrinsic information produced by said turbo decoding.
 10. The apparatus of claim 1, wherein said RS decoding implements one of belief propagation decoding and belief accumulation decoding at the variable node for RS SISO decoding.
 11. The apparatus of claim 1, wherein said RS decoding implements a min sum algorithm at the check node for RS SISO decoding.
 12. The apparatus of claim 1, wherein the communication link is a wireless communication link.
 13. A method of processing data signals transmitted on a communication link, comprising: receiving the data signals; and decoding the data signals, including performing turbo decoding and performing RS decoding; wherein said turbo decoding is based on RS decoding information produced by said RS decoding.
 14. The method of claim 13, wherein said RS decoding information includes extrinsic information produced by said RS decoding.
 15. The method of claim 14, wherein said RS decoding is based on extrinsic information produced by said turbo decoding.
 16. The method of claim 13, wherein said RS decoding is based on extrinsic information produced by said turbo decoding.
 17. The method of claim 13, including applying belief propagation to one of extrinsic information produced by said RS decoding and extrinsic information produced by said turbo decoding, and using a result of said belief propagation to produce the other of said extrinsic information.
 18. The method of claim 17, including applying further belief propagation to the other of said extrinsic information, and using a result of said further belief propagation to produce said one of said extrinsic information.
 19. The method of claim 13, including applying belief accumulation to one of extrinsic information produced by said RS decoding and extrinsic information produced by said turbo decoding, and using a result of said belief accumulation to produce the other of said extrinsic information.
 20. The method of claim 19, including applying belief propagation to the other of said extrinsic information, and using a result of said belief propagation decoding to produce said one of said extrinsic information.
 21. The method of claim 13, wherein said RS decoding includes RS SISO decoding.
 22. An apparatus for processing data signals transmitted on a communication link, comprising: means for receiving the data signals; and means for decoding the data signals, including means for performing turbo decoding and means for performing RS decoding; wherein said turbo decoding is based on RS decoding information produced by said RS decoding.
 23. A computer program product, comprising: a computer-readable medium comprising: code for causing at least one data processor to perform turbo decoding; code for causing the at least one data processor to perform RS decoding; code for causing the at least one data processor to use said turbo decoding and said RS decoding in combination to decode data signals received via a communication link; wherein said turbo decoding is based on RS decoding information produced by said RS decoding.
 24. An apparatus for processing data signals transmitted on a communication link, comprising: an input for receiving the data signals; an inner decoder coupled to said input and configured to perform inner decoding; and an outer decoder coupled to said inner decoder and configured to perform outer decoding; wherein said inner decoding is based on outer decoding information produced by said outer decoding.
 25. A method of processing data signals transmitted on a communication link, comprising: receiving the data signals; and decoding the data signals, including performing inner decoding and performing outer decoding; wherein said inner decoding is based on outer decoding information produced by said outer decoding.
 26. An apparatus for processing data signals transmitted on a communication link, comprising: means for receiving the data signals; and means for decoding the data signals, including means for performing inner decoding and means for performing outer decoding; wherein said inner decoding is based on outer decoding information produced by said outer decoding.
 27. A computer program product, comprising: a computer-readable medium comprising: code for causing at least one data processor to perform outer decoding; code for causing the at least one data processor to perform inner decoding; code for causing the at least one data processor to use said inner decoding and said outer decoding in combination to decode data signals received via a communication link; wherein said inner decoding is based on outer decoding information produced by said outer decoding. 